This R markdown file aims to provide insight into Cotton

1. Read data

First we set up the working directory to where the files are saved.

setwd('/Users/drbhomeoffice/Downloads/TICTOC_3_factor_model')   # Needs to be changed

R packages and iDEP core Functions.

if(file.exists('iDEP_core_functions.R'))
  source('iDEP_core_functions.R') else 
    source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R') 
## Warning in install.packages(...): installation of package 'runibic' had non-zero
## exit status

We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. T

inputFile <- 'Downloaded_Converted_Data.csv'  # Expression matrix
sampleInfoFile <- 'TICTOC_target_v5.csv' # Experiment design file 
geneInfoFile <- 'Cotton__ghirsutum_eg_gene_GeneInfo.csv' #Gene symbols, location etc. 
geneSetFile <- 'Cotton__ghirsutum_eg_gene.db'  # pathway database in SQL; can be GMT format 
STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'

Parameters for reading data

input_missingValue <- 'geneMedianInGroup'   #Missing values imputation method
input_dataFileFormat <- 1   #1- read counts, 2 FKPM/RPKM or DNA microarray
input_minCounts <- 0.5  #Min counts
input_NminSamples <- 1  #Minimum number of samples 
input_countsLogStart <- 4   #Pseudo count for log CPM
input_CountsTransform <- 3  #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog 
readData.out <- readData(inputFile) 
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
library(knitr)   #  install if needed. for showing tables with kable 
kable( head(readData.out$data) )    # show the first few rows of data
A68RootFlight A68RootFlight1 A68RootFlight2 A68RootFlight3 A68RootGround A68RootGround1 A68RootGround2 A68RootGround3 A68ShootFlight A68ShootFlight1 A68ShootFlight2 A68ShootFlight3 A68ShootGround A68ShootGround1 A68ShootGround2 A68ShootGround3 D130RootFlight D130RootFlight1 D130RootFlight2 D130RootFlight3 D130RootGround D130RootGround1 D130RootGround2 D130RootGround3 D130ShootFlight D130ShootFlight1 D130ShootFlight2 D130ShootFlight3 D130ShootGround D130ShootGround1 D130ShootGround2 D130ShootGround3 WTRootFlight WTRootFlight1 WTRootFlight2 WTRootFlight3 WTRootGround WTRootGround1 WTRootGround2 WTRootGround3 WTShootFlight WTShootFlight1 WTShootFlight2 WTShootFlight3 WTShootGround WTShootGround1 WTShootGround2 WTShootGround3
GOHIR.A04G039105 13.634682 12.758458 12.960225 13.463298 12.742291 13.292119 12.498714 12.645947 16.52831 16.92291 17.47056 16.95206 17.04671 16.34894 17.93003 17.48527 13.150011 13.045496 12.352965 12.928210 13.285763 12.993802 12.325954 13.531895 17.42704 17.42416 17.39902 16.43627 18.11121 17.49741 17.26551 17.29092 12.694453 13.321780 12.442504 13.314546 12.961234 12.877957 12.750915 13.048554 17.22535 17.09192 17.44060 16.93986 16.57511 17.24031 16.61856 16.91550
GOHIR.1Z078501 11.518862 11.112517 11.707862 11.588271 10.483357 10.894389 10.357086 10.407960 14.64849 15.39367 15.58573 14.97006 15.41618 14.86421 15.75047 15.70340 11.420444 11.642341 10.793313 11.290878 10.892748 10.975964 10.054120 11.511363 15.55811 15.49619 15.74476 14.85792 16.25730 15.90044 15.63570 15.39926 10.823667 11.689591 11.334193 11.577756 11.080399 10.638597 10.702579 10.772925 13.71746 15.34915 15.44428 15.41155 15.83783 15.51878 14.88232 15.11356
GOHIR.D06G053900 14.878803 15.093212 14.885349 14.858181 14.767790 14.676709 14.832845 14.274472 14.67954 14.66586 14.80640 14.60983 15.12699 14.87937 15.06388 15.30201 15.087367 15.190603 14.867747 14.643501 14.745877 14.573022 14.479369 15.151314 14.67745 14.58362 15.18286 14.40944 15.13797 15.16713 14.84991 15.45081 15.019590 14.888390 14.754578 14.887145 14.824638 14.702632 14.778814 14.636738 15.29427 14.62930 14.35350 14.52041 15.21548 15.22288 15.56396 15.54585
GOHIR.D10G234000 6.188859 4.172852 3.647304 4.800828 5.152338 4.820082 5.508477 4.265765 14.94326 15.28464 14.56165 13.06029 14.69524 14.34236 15.65679 15.53915 4.944460 4.618055 4.470293 5.130398 5.529630 6.020457 4.654963 6.126543 14.90263 13.90516 14.63638 14.67551 15.96825 15.50151 15.26764 15.33284 3.893682 5.190760 3.173068 5.162571 3.944543 4.641883 5.922388 6.285947 14.53945 14.82237 15.30911 14.37597 14.39731 15.28010 15.17759 15.49376
GOHIR.D07G229800 13.300747 13.789650 12.740293 13.011281 13.209627 12.706946 13.757271 13.720693 10.46036 11.99670 11.47402 11.28924 11.96631 11.91818 11.04302 11.46824 13.017612 12.255448 14.327929 13.503478 13.408115 13.793294 13.457550 13.625095 11.78292 10.99969 11.06758 10.87697 10.00751 11.80852 10.95325 11.42136 14.255679 12.406634 13.070186 13.059174 14.116479 13.373579 13.254336 14.169017 10.80831 10.96493 9.82251 10.45029 11.61730 11.58556 11.81137 11.61777
GOHIR.A10G221700 4.216131 2.846017 3.317729 2.413137 2.780192 3.201010 3.716861 2.157596 14.59949 15.00590 13.81110 12.42347 14.30451 13.96907 15.36392 15.27729 2.504485 2.480601 2.191467 3.601488 4.213318 3.764766 3.733824 2.912381 14.41470 13.25574 13.98855 14.33476 15.75930 15.24897 15.09217 15.10696 2.240076 3.625201 2.354868 2.658545 3.360104 3.143764 4.490900 3.492852 13.66264 14.39655 15.10240 13.83723 13.64358 14.98933 14.80109 15.27811 
readSampleInfo.out <- readSampleInfo(sampleInfoFile) 
## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
## incomplete final line found by readTableHeader on 'TICTOC_target_v5.csv'
kable( readSampleInfo.out )
Treatment Genotype Tissue
A68RootFlight Flight A68 Root
A68RootFlight1 Flight A68 Root
A68RootFlight2 Flight A68 Root
A68RootFlight3 Flight A68 Root
A68RootGround Ground A68 Root
A68RootGround1 Ground A68 Root
A68RootGround2 Ground A68 Root
A68RootGround3 Ground A68 Root
A68ShootFlight Flight A68 Shoot
A68ShootFlight1 Flight A68 Shoot
A68ShootFlight2 Flight A68 Shoot
A68ShootFlight3 Flight A68 Shoot
A68ShootGround Ground A68 Shoot
A68ShootGround1 Ground A68 Shoot
A68ShootGround2 Ground A68 Shoot
A68ShootGround3 Ground A68 Shoot
D130RootFlight Flight D130 Root
D130RootFlight1 Flight D130 Root
D130RootFlight2 Flight D130 Root
D130RootFlight3 Flight D130 Root
D130RootGround Ground D130 Root
D130RootGround1 Ground D130 Root
D130RootGround2 Ground D130 Root
D130RootGround3 Ground D130 Root
D130ShootFlight Flight D130 Shoot
D130ShootFlight1 Flight D130 Shoot
D130ShootFlight2 Flight D130 Shoot
D130ShootFlight3 Flight D130 Shoot
D130ShootGround Ground D130 Shoot
D130ShootGround1 Ground D130 Shoot
D130ShootGround2 Ground D130 Shoot
D130ShootGround3 Ground D130 Shoot
WTRootFlight Flight WT Root
WTRootFlight1 Flight WT Root
WTRootFlight2 Flight WT Root
WTRootFlight3 Flight WT Root
WTRootGround Ground WT Root
WTRootGround1 Ground WT Root
WTRootGround2 Ground WT Root
WTRootGround3 Ground WT Root
WTShootFlight Flight WT Shoot
WTShootFlight1 Flight WT Shoot
WTShootFlight2 Flight WT Shoot
WTShootFlight3 Flight WT Shoot
WTShootGround Ground WT Shoot
WTShootGround1 Ground WT Shoot
WTShootGround2 Ground WT Shoot
WTShootGround3 Ground WT Shoot 
input_selectOrg ="NEW" 
input_selectGO <- 'All' #Gene set category 
input_noIDConversion = TRUE  
allGeneInfo.out <- geneInfo(geneInfoFile) 
converted.out = NULL 
convertedData.out <- convertedData()     
nGenesFilter()  
## [1] "54267 genes in 48 samples. 54267  genes passed filter.\n Original gene IDs used."
convertedCounts.out <- convertedCounts()  # converted counts, just for compatibility

2. Pre-process

# Read counts per library 
parDefault = par() 
par(mar=c(12,4,2,2)) 
# barplot of total read counts
x <- readData.out$rawCounts
groups = as.factor( detectGroups(colnames(x ) ) )
if(nlevels(groups)<=1 | nlevels(groups) >20 )  
  col1 = 'green'  else
    col1 = rainbow(nlevels(groups))[ groups ]               

barplot( colSums(x)/1e6, 
         col=col1,las=3, main="Total read counts (millions)")  

readCountsBias()  # detecting bias in sequencing depth 
## [1] "Warning! Sequencing depth bias detected. Total read counts are significantly different among sample groups (p= 7.67e-08 ) based on ANOVA.  Total read counts seem to be correlated with factor Tissue (p= 4.38e-13 ).  "
# Box plot 
x = readData.out$data 
boxplot(x, las = 2, col=col1,
        ylab='Transformed expression levels',
        main='Distribution of transformed data') 

#Density plot 
par(parDefault) 
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
densityPlot()       

# Scatter plot of the first two samples 
plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2], 
     main='Scatter plot of first two samples') 

####plot gene or gene family
input_selectOrg ="BestMatch" 
input_geneSearch <- 'HOXA1;e2f2;tp53'   #Gene ID for searching 
genePlot()  
## NULL
input_useSD <- 'FALSE'  #Use standard deviation instead of standard error in error bar? 
geneBarPlotError()       
## NULL

3. Heatmap

# hierarchical clustering tree
x <- readData.out$data
maxGene <- apply(x,1,max)
# remove bottom 25% lowly expressed genes, which inflate the PPC
x <- x[which(maxGene > quantile(maxGene)[1] ) ,] 
plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle") 

#Correlation matrix
input_labelPCC <- TRUE  #Show correlation coefficient? 
correlationMatrix() 
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.

# Parameters for heatmap
input_nGenes <- 100 #Top genes for heatmap
input_geneCentering <- TRUE #centering genes ?
input_sampleCentering <- TRUE   #Center by sample?
input_geneNormalize <- TRUE #Normalize by gene?
input_sampleNormalize <- TRUE   #Normalize by sample?
input_noSampleClustering <- FALSE   #Use original sample order
input_heatmapCutoff <- 4    #Remove outliers beyond number of SDs 
input_distFunctions <- 2    #which distant funciton to use
input_hclustFunctions <- 1  #Linkage type
input_heatColors1 <- 2  #Colors
input_selectFactorsHeatmap <- 'Sample_Name' #Sample coloring factors 
png('heatmap.png', width = 10, height = 15, units = 'in', res = 300) 
staticHeatmap() 
dev.off()  
## quartz_off_screen 
##                 2

[heatmap] (heatmap.png)

heatmapPlotly() # interactive heatmap using Plotly

4. K-means clustering

input_nGenesKNN <- 2000 #Number of genes fro k-Means
input_nClusters <- 4    #Number of clusters 
maxGeneClustering = 12000
input_kmeansNormalization <- 'geneMean' #Normalization
input_KmeansReRun <- 0  #Random seed 

distributionSD()  #Distribution of standard deviations 

KmeansNclusters()  #Number of clusters 

Kmeans.out = Kmeans()   #Running K-means 
KmeansHeatmap()   #Heatmap for k-Means 

#Read gene sets for enrichment analysis 
sqlite  <- dbDriver('SQLite')
input_selectGO3 <- 'GOBP'   #Gene set category
input_minSetSize <- 15  #Min gene set size
input_maxSetSize <- 2000    #Max gene set size 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO3,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  )  
# Alternatively, users can use their own GMT files by
#GeneSets.out <- readGMTRobust('somefile.GMT')  
results <- KmeansGO()  #Enrichment analysis for k-Means clusters    
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
Cluster adj.Pval Genes Pathways
A 2.54e-08 12 Photosynthesis
3.53e-03 5 Nitrogen compound metabolic process
7.82e-03 14 Proteolysis
B 1.21e-61 34 Photosynthesis-light harvesting
5.23e-43 34 Photosynthesis
4.72e-04 7 Glycolytic process
C 5.10e-14 29 Transport
3.43e-09 10 Response to biotic stimulus
5.10e-07 10 Defense response
9.82e-06 12 Response to oxidative stress
D 3.73e-24 114 Translation
1.43e-08 23 Glycolytic process
4.71e-06 13 Proton transport
4.96e-06 9 Translational elongation
1.33e-05 24 Vesicle-mediated transport
4.41e-05 7 S-adenosylmethionine biosynthetic process
7.53e-05 16 Microtubule-based process
8.82e-05 27 Protein folding
8.82e-05 7 Cellular amino acid biosynthetic process
5.33e-04 7 ATP metabolic process 
input_seedTSNE <- 0 #Random seed for t-SNE
input_colorGenes <- TRUE    #Color genes in t-SNE plot? 
tSNEgenePlot()  #Plot genes using t-SNE

5. PCA and beyond

input_selectFactors <- 'Tissue' #Factor coded by color
input_selectFactors2 <- 'Sample_Name'   #Factor coded by shape
input_tsneSeed2 <- 0    #Random seed for t-SNE 
#PCA, MDS and t-SNE plots
PCAplot()  
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 12. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 24 rows containing missing values (geom_point).

MDSplot() 
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 12. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 24 rows containing missing values (geom_point).

tSNEplot()  
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 12. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 24 rows containing missing values (geom_point).

#Read gene sets for pathway analysis using PGSEA on principal components
input_selectGO6 <- 'All'    #Gene set category 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO6,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  )  
PCApathway() # Run PGSEA analysis 
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
##   version 3.12

cat( PCA2factor() )   #The correlation between PCs with factors 
## 
##  Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Tissue (p=3.14e-47).
## PC2 is correlated with Treatment (p=9.39e-05).
## PC4 is correlated with Treatment (p=4.01e-04).

6. DEG1

input_CountsDEGMethod <- 3  #DESeq2= 3,limma-voom=2,limma-trend=1 
input_limmaPval <- 0.1  #FDR cutoff
input_limmaFC <- 2  #Fold-change cutoff
input_selectModelComprions <- c('Treatment: Flight vs. Ground','Treatment: Ground vs. Flight','Genotype: A68 vs. D130','Genotype: A68 vs. WT','Genotype: D130 vs. A68','Genotype: D130 vs. WT','Tissue: Root vs. Shoot','Tissue: Shoot vs. Root')   #Selected comparisons
input_selectFactorsModel <- c('Treatment','Genotype','Tissue')  #Selected comparisons
input_selectInteractions <- c('Treatment:Genotype','Treatment:Tissue','Genotype:Tissue')    #Selected comparisons
input_selectBlockFactorsModel <- NULL   #Selected comparisons
factorReferenceLevels.out <- c('Treatment:Ground','Genotype:WT','Tissue:Root') 

limma.out <- limma()
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
DEG.data.out <- DEG.data()
## Warning in merge.data.frame(genes, convertedData.out, by = "row.names"): column
## names 'A68-WT_for_Flight', 'A68-WT_for_Shoot', 'D130-WT_for_Flight', 'D130-
## WT_for_Shoot' are duplicated in the result
limma.out$comparisons 
##  [1] "Flight-Ground"                    "Ground-Flight"                   
##  [3] "A68-D130"                         "A68-WT"                          
##  [5] "D130-A68"                         "D130-WT"                         
##  [7] "Root-Shoot"                       "Shoot-Root"                      
##  [9] "I:Treatment_Flight.Genotype_A68"  "I:Treatment_Flight.Genotype_D130"
## [11] "I:Treatment_Flight.Tissue_Shoot"  "I:Genotype_A68.Tissue_Shoot"     
## [13] "I:Genotype_D130.Tissue_Shoot"     "Flight-Ground_for_A68"           
## [15] "Flight-Ground_for_D130"           "Flight-Ground_for_Shoot"         
## [17] "A68-WT_for_Flight"                "A68-WT_for_Flight"               
## [19] "A68-WT_for_Shoot"                 "A68-WT_for_Shoot"                
## [21] "D130-WT_for_Flight"               "D130-WT_for_Flight"              
## [23] "D130-WT_for_Shoot"                "D130-WT_for_Shoot"               
## [25] "Shoot-Root_for_Flight"            "Shoot-Root_for_A68"              
## [27] "Shoot-Root_for_D130"
input_selectComparisonsVenn <- c('Flight-Ground','A68-D130','A68-WT','D130-WT','Root-Shoot')    #Selected comparisons for Venn diagram
input_UpDownRegulated <- FALSE  #Split up and down regulated genes 
vennPlot() # Venn diagram 

sigGeneStats() # number of DEGs as figure 

sigGeneStatsTable() # number of DEGs as table 
##                                                       Comparisons    Up  Down
## Flight.Ground                                       Flight-Ground  5257  4038
## Ground.Flight                                       Ground-Flight  4038  5257
## A68.D130                                                 A68-D130     8    30
## A68.WT                                                     A68-WT     7    13
## D130.A68                                                 D130-A68    30     8
## D130.WT                                                   D130-WT    23     4
## Root.Shoot                                             Root-Shoot 12872 11623
## Shoot.Root                                             Shoot-Root 11623 12872
## I.Treatment_Flight.Genotype_A68   I:Treatment_Flight-Genotype_A68    32   353
## I.Treatment_Flight.Genotype_D130 I:Treatment_Flight-Genotype_D130   101   777
## I.Treatment_Flight.Tissue_Shoot   I:Treatment_Flight-Tissue_Shoot  4633  5614
## I.Genotype_A68.Tissue_Shoot           I:Genotype_A68-Tissue_Shoot     5     2
## I.Genotype_D130.Tissue_Shoot         I:Genotype_D130-Tissue_Shoot     3     6
## Flight.Ground_for_A68                       Flight-Ground_for_A68  4655  5896
## Flight.Ground_for_D130                     Flight-Ground_for_D130  3431  3451
## Flight.Ground_for_Shoot                   Flight-Ground_for_Shoot  1027  1391
## A68.WT_for_Flight                               A68-WT_for_Flight    70  1185
## A68.WT_for_Flight.1                             A68-WT_for_Flight    26   743
## A68.WT_for_Shoot                                 A68-WT_for_Shoot     8    10
## A68.WT_for_Shoot.1                               A68-WT_for_Shoot     4    19
## D130.WT_for_Flight                             D130-WT_for_Flight    60   327
## D130.WT_for_Flight.1                           D130-WT_for_Flight   113  1066
## D130.WT_for_Shoot                               D130-WT_for_Shoot    11     2
## D130.WT_for_Shoot.1                             D130-WT_for_Shoot     8     5
## Shoot.Root_for_Flight                       Shoot-Root_for_Flight 10558 11274
## Shoot.Root_for_A68                             Shoot-Root_for_A68 11333 12708
## Shoot.Root_for_D130                           Shoot-Root_for_D130 10839 13038

7. DEG2

input_selectContrast <- 'Flight-Ground' #Selected comparisons 
selectedHeatmap.data.out <- selectedHeatmap.data()
selectedHeatmap()   # heatmap for DEGs in selected comparison

# Save gene lists and data into files
write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv') 
write.csv(DEG.data(),'DEG.data.csv' )
## Warning in merge.data.frame(genes, convertedData.out, by = "row.names"): column
## names 'A68-WT_for_Flight', 'A68-WT_for_Shoot', 'D130-WT_for_Flight', 'D130-
## WT_for_Shoot' are duplicated in the result
write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
input_selectGO2 <- 'All'    #Gene set category 
geneListData.out <- geneListData()  
volcanoPlot()  

scatterPlot()  

MAplot()  

geneListGOTable.out <- geneListGOTable()  
# Read pathway data again 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO2,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  ) 
input_removeRedudantSets <- TRUE    #Remove highly redundant gene sets? 
results <- geneListGO()  #Enrichment analysis
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
Direction adj.Pval nGenes Pathways
Down regulated 2.7e-48 282 Translation
2.6e-47 272 Ribosome
2.6e-47 276 Structural constituent of ribosome
3.2e-15 191 Intracellular
6.3e-03 9 5S rRNA binding
1.0e-02 42 Microtubule-based movement
1.0e-02 42 Kinesin complex
1.0e-02 42 Microtubule motor activity
Up regulated 2.0e-09 279 Transcription factor activity-sequence-specific DNA binding
7.6e-09 106 ADP binding
1.3e-06 35 Recognition of pollen
2.0e-04 12 Coenzyme A metabolic process
2.0e-04 12 Hydroxymethylglutaryl-CoA reductase (NADPH) activity
2.5e-04 134 Oxidoreductase activity-acting on paired donors-with incorporation or reduction of molecular oxygen
8.3e-04 155 Sequence-specific DNA binding
1.5e-03 22 Terpene synthase activity
5.1e-03 142 Iron ion binding
5.1e-03 29 Polysaccharide binding

7. DEG2 .2 Genotype: D130 vs. WT

input_selectContrast <- 'D130-WT'   #Selected comparisons 
selectedHeatmap.data.out <- selectedHeatmap.data()
selectedHeatmap()   # heatmap for DEGs in selected comparison

# Save gene lists and data into files
write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv') 
write.csv(DEG.data(),'DEG.data.csv' )
## Warning in merge.data.frame(genes, convertedData.out, by = "row.names"): column
## names 'A68-WT_for_Flight', 'A68-WT_for_Shoot', 'D130-WT_for_Flight', 'D130-
## WT_for_Shoot' are duplicated in the result
write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
input_selectGO2 <- 'All'    #Gene set category 
geneListData.out <- geneListData()  
volcanoPlot()  

scatterPlot()  

MAplot()

#{r, message=FALSE } #geneListGOTable.out <- geneListGOTable() ## Read pathway data again #GeneSets.out <-readGeneSets( geneSetFile, # convertedData.out, input_selectGO2,input_selectOrg, # c(input_minSetSize, input_maxSetSize) ) #input_removeRedudantSets <- TRUE #Remove highly redundant gene sets? #results <- geneListGO() #Enrichment analysis #results$adj.Pval <- format( results$adj.Pval,digits=3 ) #kable( results, row.names=FALSE) # ## 7. DEG2 .3 Genotype: A68 vs. D130

input_selectContrast3 <- 'A68-D130' #Selected comparisons 
selectedHeatmap.data.out <- selectedHeatmap.data()
selectedHeatmap()   # heatmap for DEGs in selected comparison

# Save gene lists and data into files
write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv') 
write.csv(DEG.data(),'DEG.data.csv' )
## Warning in merge.data.frame(genes, convertedData.out, by = "row.names"): column
## names 'A68-WT_for_Flight', 'A68-WT_for_Shoot', 'D130-WT_for_Flight', 'D130-
## WT_for_Shoot' are duplicated in the result
write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
input_selectGO2 <- 'All'    #Gene set category 
geneListData.out <- geneListData()  
volcanoPlot()  

scatterPlot()  

MAplot()

#{r, message=FALSE } #geneListGOTable.out <- geneListGOTable() ## Read pathway data again #GeneSets.out <-readGeneSets( geneSetFile, # convertedData.out, input_selectGO2,input_selectOrg, # c(input_minSetSize, input_maxSetSize) ) #input_removeRedudantSets <- TRUE #Remove highly redundant gene sets? #results <- geneListGO() #Enrichment analysis #results$adj.Pval <- format( results$adj.Pval,digits=3 ) #kable( results, row.names=FALSE) #

8. Pathway analysis 1

input_selectContrast1 <- 'Flight-Ground'    #select Comparison 
#input_selectContrast1 = limma.out$comparisons[3] # manually set
input_selectGO <- 'All' #Gene set category 
#input_selectGO='custom' # if custom gmt file
input_minSetSize <- 15  #Min size for gene set
input_maxSetSize <- 2000    #Max size for gene set 
# Read pathway data again 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  ) 
input_pathwayPvalCutoff <- 0.2  #FDR cutoff
input_nPathwayShow <- 30    #Top pathways to show
input_absoluteFold <- FALSE #Use absolute values of fold-change?
input_GenePvalCutoff <- 1   #FDR to remove genes 

input_pathwayMethod = 1  # 1  GAGE
gagePathwayData.out <- gagePathwayData()  # pathway analysis using GAGE  

results <- gagePathwayData.out  #Enrichment analysis for k-Means clusters   
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
Direction GAGE analysis: Flight vs Ground statistic Genes adj.Pval
Down Microtubule binding -7.7175 205 2.9e-11
Microtubule-based movement -7.1905 178 2.8e-10
Kinesin complex -7.1905 178 2.8e-10
Microtubule motor activity -7.1905 178 2.8e-10
RNA binding -6.9148 469 4.0e-10
DNA replication -5.9882 90 6.2e-07
Protein folding -5.6311 231 1.1e-06
Large ribosomal subunit -4.7963 44 2.6e-04
Ribosome biogenesis -4.7448 44 2.8e-04
Microtubule -4.7108 78 1.6e-04
RRNA binding -4.4645 36 8.1e-04
Developmental process -4.4298 35 6.8e-04
Photosynthesis-light harvesting -4.2408 48 9.5e-04
Oxidoreductase activity-acting on the CH-OH group of donors-NAD or NADP as acceptor -4.2076 163 6.8e-04
Steroid biosynthetic process -4.07 82 1.1e-03
RRNA processing -4.0136 42 2.0e-03
Microtubule-based process -3.9531 93 1.5e-03
GTP binding -3.8797 488 1.5e-03
DNA repair -3.8606 178 1.7e-03
3-beta-hydroxy-delta5-steroid dehydrogenase activity -3.7329 78 2.9e-03
Glycolytic process -3.7293 105 2.8e-03
Up ADP binding 10.0101 326 1.1e-19
Ubiquitin-protein transferase activity 6.401 208 4.8e-08
Protein ubiquitination 6.144 182 1.6e-07
Recognition of pollen 6.0234 76 6.0e-07
Signal transduction 5.8813 282 3.9e-07
Protein serine/threonine phosphatase activity 4.7688 212 9.5e-05
Protein dephosphorylation 4.3147 267 6.1e-04
Coenzyme A metabolic process 4.2011 17 5.1e-03
Polysaccharide binding 3.9743 83 3.1e-03 
pathwayListData.out = pathwayListData() 
enrichmentPlot(pathwayListData.out, 25  ) 

enrichmentNetwork(pathwayListData.out )  
## Warning: package 'igraph' was built under R version 4.0.5

enrichmentNetworkPlotly(pathwayListData.out) 

input_pathwayMethod = 3  # 1  fgsea 
fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea 
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
results <- fgseaPathwayData.out  #Enrichment analysis for k-Means clusters  
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
Direction GSEA analysis: Flight vs Ground NES Genes adj.Pval
Down Structural constituent of ribosome -3.5389 831 1.6e-02
Ribosome -3.5334 814 1.6e-02
Translation -3.5273 847 1.8e-02
Intracellular -2.9788 778 1.5e-02
Microtubule-based movement -2.6541 178 4.5e-03
Kinesin complex -2.6541 178 4.5e-03
Microtubule motor activity -2.6541 178 4.5e-03
Microtubule binding -2.6461 205 4.8e-03
DNA replication -2.6188 90 3.8e-03
Large ribosomal subunit -2.4559 44 3.8e-03
Developmental process -2.4555 35 3.8e-03
Ribosome biogenesis -2.3586 44 3.8e-03
Photosynthesis-light harvesting -2.3279 48 3.8e-03
Microtubule -2.3163 78 3.8e-03
DNA helicase activity -2.2633 16 3.8e-03
5S rRNA binding -2.2631 16 3.8e-03
RRNA processing -2.2192 42 3.8e-03
RRNA binding -2.2009 36 3.8e-03
Steroid biosynthetic process -2.1624 82 3.8e-03
Protein folding -2.1429 231 5.1e-03
Up ADP binding 2.6467 326 3.7e-03
Protein ubiquitination 2.4683 182 3.7e-03
Recognition of pollen 2.4474 76 3.7e-03
Ubiquitin-protein transferase activity 2.4452 208 3.7e-03
Terpene synthase activity 2.3996 52 3.7e-03
Transcription factor activity-sequence-specific DNA binding 2.2765 1086 3.7e-03
Lyase activity 2.2518 69 3.7e-03
Oxidoreductase activity-acting on paired donors-with incorporation or reduction of molecular oxygen 2.1721 528 3.7e-03
Signal transduction 2.1492 282 3.7e-03
Coenzyme A metabolic process 2.1383 17 3.7e-03 
pathwayListData.out = pathwayListData() 
enrichmentPlot(pathwayListData.out, 25  ) 

enrichmentNetwork(pathwayListData.out )  

enrichmentNetworkPlotly(pathwayListData.out) 

PGSEAplot() # pathway analysis using PGSEA 
## 
## Computing P values using ANOVA

8. Pathway analysis 2

input_selectContrast1 <- 'D130-WT'  #select Comparison 
#input_selectContrast1 = limma.out$comparisons[3] # manually set
input_selectGO <- 'All' #Gene set category 
#input_selectGO='custom' # if custom gmt file
input_minSetSize <- 15  #Min size for gene set
input_maxSetSize <- 2000    #Max size for gene set 
# Read pathway data again 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  ) 
input_pathwayPvalCutoff <- 0.2  #FDR cutoff
input_nPathwayShow <- 30    #Top pathways to show
input_absoluteFold <- FALSE #Use absolute values of fold-change?
input_GenePvalCutoff <- 1   #FDR to remove genes 

input_pathwayMethod = 1  # 1  GAGE
gagePathwayData.out <- gagePathwayData()  # pathway analysis using GAGE  

results <- gagePathwayData.out  #Enrichment analysis for k-Means clusters   
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
Direction GAGE analysis: D130 vs WT statistic Genes adj.Pval
Down Microtubule binding -6.1231 205 5.8e-07
Microtubule-based movement -5.6978 178 1.6e-06
Kinesin complex -5.6978 178 1.6e-06
Microtubule motor activity -5.6978 178 1.6e-06
DNA replication -4.7104 90 3.1e-04
RNA binding -4.0344 469 2.3e-03
Developmental process -3.7998 35 1.0e-02
Nucleosome -3.2039 74 4.5e-02
Transcription-DNA-templated -3.1785 287 4.4e-02
DNA-directed RNA polymerase activity -3.1047 116 4.9e-02
Ribosome biogenesis -3.0998 44 5.9e-02
Regulation of cell cycle -3.037 16 9.3e-02
DNA-directed DNA polymerase activity -2.9906 25 9.3e-02
Nucleosome assembly -2.8511 49 9.4e-02
RNA processing -2.7275 99 1.1e-01
RRNA processing -2.7177 42 1.2e-01
Up Response to oxidative stress 4.8396 215 3.2e-04
Peroxidase activity 4.7537 193 3.2e-04
Hydrolase activity-acting on ester bonds 4.5817 194 4.8e-04
Photosynthesis 4.0598 126 3.7e-03
ATP hydrolysis coupled proton transport 3.4486 80 3.9e-02
Photosystem II 3.1583 70 7.3e-02
Oxidoreductase activity-acting on NAD(P)H-oxygen as acceptor 3.053 19 1.2e-01
Transferase activity-transferring acyl groups other than amino-acyl groups 3.0043 178 9.3e-02
Transporter activity 2.8052 315 1.3e-01
Vesicle-mediated transport 2.7883 167 1.3e-01
Transferase activity-transferring glycosyl groups 2.6948 176 1.5e-01
Oxidoreductase activity-acting on the CH-OH group of donors-NAD or NADP as acceptor 2.6086 163 1.8e-01
Photosystem I 2.5493 38 1.9e-01
Response to biotic stimulus 2.5096 58 1.9e-01 
pathwayListData.out = pathwayListData() 
enrichmentPlot(pathwayListData.out, 25  ) 

enrichmentNetwork(pathwayListData.out )  

enrichmentNetworkPlotly(pathwayListData.out) 

input_pathwayMethod = 3  # 1  fgsea 
fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea 
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
results <- fgseaPathwayData.out  #Enrichment analysis for k-Means clusters  
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
Direction GSEA analysis: D130 vs WT NES Genes adj.Pval
Down Microtubule binding -2.3294 205 4.4e-03
Microtubule-based movement -2.2983 178 4.4e-03
Kinesin complex -2.2983 178 4.4e-03
Microtubule motor activity -2.2983 178 4.4e-03
DNA replication -2.262 90 4.4e-03
Developmental process -2.261 35 4.4e-03
Regulation of cell cycle -2.0954 16 4.4e-03
Structural constituent of ribosome -1.9737 831 4.7e-03
Translation -1.9734 847 4.7e-03
Ribosome -1.9613 814 4.7e-03
Ribosome biogenesis -1.9494 44 4.4e-03
Nucleosome -1.9333 74 4.4e-03
DNA-directed DNA polymerase activity -1.9234 25 4.4e-03
Nucleus -1.9036 1153 4.7e-03
RRNA processing -1.8722 42 5.5e-03
Nucleosome assembly -1.8691 49 5.5e-03
Up Hydrolase activity-acting on ester bonds 2.2236 194 4.4e-03
Peroxidase activity 2.1871 193 4.4e-03
Response to oxidative stress 2.1465 215 4.4e-03
Heme binding 2.0168 734 4.4e-03
Lipid biosynthetic process 2.0165 34 4.4e-03
Photosynthesis 2.0145 126 4.4e-03
ATP hydrolysis coupled proton transport 1.9936 80 4.4e-03
Photosystem II 1.9824 70 4.4e-03
Transferase activity-transferring acyl groups other than amino-acyl groups 1.942 178 4.4e-03
Ionotropic glutamate receptor activity 1.9325 25 6.4e-03
Photosystem I 1.9179 38 6.4e-03
Oxidoreductase activity-acting on NAD(P)H-oxygen as acceptor 1.8979 19 1.1e-02
Diacylglycerol O-acyltransferase activity 1.8885 18 1.1e-02
Plasma membrane 1.8701 22 1.3e-02 
pathwayListData.out = pathwayListData() 
enrichmentPlot(pathwayListData.out, 25  ) 

enrichmentNetwork(pathwayListData.out )  

enrichmentNetworkPlotly(pathwayListData.out) 

PGSEAplot() # pathway analysis using PGSEA 
## 
## Computing P values using ANOVA

8. Pathway analysis 3

input_selectContrast1 <- 'A68-D130' #select Comparison 
#input_selectContrast1 = limma.out$comparisons[3] # manually set
input_selectGO <- 'All' #Gene set category 
#input_selectGO='custom' # if custom gmt file
input_minSetSize <- 15  #Min size for gene set
input_maxSetSize <- 2000    #Max size for gene set 
# Read pathway data again 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  ) 
input_pathwayPvalCutoff <- 0.2  #FDR cutoff
input_nPathwayShow <- 30    #Top pathways to show
input_absoluteFold <- FALSE #Use absolute values of fold-change?
input_GenePvalCutoff <- 1   #FDR to remove genes 

input_pathwayMethod = 1  # 1  GAGE
gagePathwayData.out <- gagePathwayData()  # pathway analysis using GAGE  

results <- gagePathwayData.out  #Enrichment analysis for k-Means clusters   
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
Direction GAGE analysis: A68 vs D130 statistic Genes adj.Pval
Down Photosynthesis -6.6622 126 3.9e-08
Photosystem I -5.1343 38 5.3e-04
Photosystem II -4.4338 70 1.4e-03
ADP binding -4.0641 321 3.0e-03
Photosystem II oxygen evolving complex -3.9617 42 4.8e-03
Cellular glucan metabolic process -3.8804 63 4.8e-03
Apoplast -3.8804 63 4.8e-03
Xyloglucan:xyloglucosyl transferase activity -3.8804 63 4.8e-03
Transporter activity -3.5841 313 9.2e-03
Extrinsic component of membrane -3.5054 37 1.8e-02
Photosystem I reaction center -3.446 20 3.7e-02
Transferase activity-transferring hexosyl groups -3.1089 211 3.8e-02
Recognition of pollen -3.0578 76 4.6e-02
Protein serine/threonine phosphatase activity -2.9539 209 5.3e-02
Transferase activity-transferring acyl groups other than amino-acyl groups -2.8833 178 6.3e-02
Peroxidase activity -2.8319 191 6.3e-02
Up Microtubule binding 5.8004 205 3.3e-06
Microtubule-based movement 5.5175 178 4.0e-06
Kinesin complex 5.5175 178 4.0e-06
Microtubule motor activity 5.5175 178 4.0e-06
DNA replication 5.407 90 1.4e-05
RNA binding 4.9775 467 3.0e-05
Developmental process 4.0311 35 4.6e-03
DNA-directed RNA polymerase activity 3.7816 116 5.7e-03
Ribosome biogenesis 3.6746 44 1.2e-02
Endoplasmic reticulum 3.3901 107 1.9e-02
Transcription-DNA-templated 3.311 285 2.0e-02
DNA repair 3.0334 176 5.0e-02
Intracellular protein transport 2.9607 274 5.6e-02
RNA processing 2.9589 99 5.6e-02 
pathwayListData.out = pathwayListData() 
enrichmentPlot(pathwayListData.out, 25  ) 

enrichmentNetwork(pathwayListData.out )  

enrichmentNetworkPlotly(pathwayListData.out) 

input_pathwayMethod = 3  # 1  fgsea 
fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea 
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
results <- fgseaPathwayData.out  #Enrichment analysis for k-Means clusters  
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
Direction GSEA analysis: A68 vs D130 NES Genes adj.Pval
Down Photosynthesis -2.4525 126 3.8e-03
Photosystem I -2.2795 38 3.8e-03
Photosystem II -2.1876 70 3.8e-03
Cellular glucan metabolic process -2.1836 63 3.8e-03
Apoplast -2.1836 63 3.8e-03
Xyloglucan:xyloglucosyl transferase activity -2.1836 63 3.8e-03
Photosystem II oxygen evolving complex -2.1085 42 5.0e-03
Polysaccharide catabolic process -2.0837 30 3.8e-03
Beta-amylase activity -2.0837 30 3.8e-03
Lipid biosynthetic process -2.0395 34 5.0e-03
Extrinsic component of membrane -2.0365 37 3.8e-03
2 iron-2 sulfur cluster binding -1.9485 26 6.2e-03
Transferase activity-transferring acyl groups other than amino-acyl groups -1.9401 178 3.8e-03
Recognition of pollen -1.9392 76 3.8e-03
Heme binding -1.9022 730 3.8e-03
Up Ribosome 2.6395 812 8.6e-03
Translation 2.6305 845 8.6e-03
Structural constituent of ribosome 2.6145 829 8.6e-03
DNA replication 2.4602 90 4.9e-03
Developmental process 2.3295 35 4.6e-03
Microtubule-based movement 2.2128 178 5.0e-03
Kinesin complex 2.2128 178 5.0e-03
Microtubule motor activity 2.2128 178 5.0e-03
Microtubule binding 2.208 205 5.1e-03
RRNA binding 2.0875 36 4.6e-03
Ribosome biogenesis 2.0695 44 4.6e-03
Intracellular 2.0438 775 8.6e-03
5S rRNA binding 2.0024 16 4.6e-03
RRNA processing 1.9639 42 6.2e-03
Pseudouridine synthesis 1.9441 43 6.2e-03 
pathwayListData.out = pathwayListData() 
enrichmentPlot(pathwayListData.out, 25  ) 

enrichmentNetwork(pathwayListData.out )  

enrichmentNetworkPlotly(pathwayListData.out) 

PGSEAplot() # pathway analysis using PGSEA 
## 
## Computing P values using ANOVA

9. Chromosome 1

input_selectContrast2 <- 'Flight-Ground'    #select Comparison 
#input_selectContrast2 = limma.out$comparisons[3] # manually set
input_limmaPvalViz <- 0.1   #FDR to filter genes
input_limmaFCViz <- 2   #FDR to filter genes 
genomePlotly() # shows fold-changes on the genome 
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion
## Warning in max(chromosomesNumbers, na.rm = T): no non-missing arguments to max;
## returning -Inf

9. Chromosome 2

input_selectContrast2 <- 'D130-WT'  #select Comparison 
#input_selectContrast2 = limma.out$comparisons[3] # manually set
input_limmaPvalViz <- 0.1   #FDR to filter genes
input_limmaFCViz <- 2   #FDR to filter genes 
genomePlotly() # shows fold-changes on the genome 
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion
## Warning in max(chromosomesNumbers, na.rm = T): no non-missing arguments to max;
## returning -Inf

9. Chromosome 3

input_selectContrast2 <- 'A68-D130' #select Comparison 
#input_selectContrast2 = limma.out$comparisons[3] # manually set
input_limmaPvalViz <- 0.1   #FDR to filter genes
input_limmaFCViz <- 2   #FDR to filter genes 
genomePlotly() # shows fold-changes on the genome 
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion
## Warning in max(chromosomesNumbers, na.rm = T): no non-missing arguments to max;
## returning -Inf

10. Biclustering

input_nGenesBiclust <- 1000 #Top genes for biclustering
input_biclustMethod <- 'BCCC()' #Method: 'BCCC', 'QUBIC', 'runibic' ... 
biclustering.out = biclustering()  # run analysis
## Warning: package 'MASS' was built under R version 4.0.5
## Warning: package 'colorspace' was built under R version 4.0.5
input_selectBicluster <- 1  #select a cluster 
biclustHeatmap()   # heatmap for selected cluster 

input_selectGO4 <- 'All'    #Gene set category 
# Read pathway data again 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO4,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  )  
results <- geneListBclustGO()  #Enrichment analysis for k-Means clusters    
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
adj.Pval Genes Pathways
1.1e-09 17 Unfolded protein binding
9.5e-07 13 Microtubule
2.9e-06 7 Cellular amino acid biosynthetic process
4.3e-06 13 Microtubule-based process
1.9e-05 6 Clathrin coat of trans-Golgi network vesicle
1.9e-05 6 Clathrin coat of coated pit
2.4e-04 17 Protein folding
2.4e-04 13 GTPase activity
3.2e-04 11 Glycolytic process
3.2e-04 5 S-adenosylmethionine biosynthetic process

10. Biclustering 2

input_nGenesBiclust <- 1000 #Top genes for biclustering
input_biclustMethod <- 'BCCC()' #Method: 'BCCC', 'QUBIC', 'runibic' ... 
biclustering.out = biclustering()  # run analysis

input_selectBicluster <- 2  #select a cluster 
biclustHeatmap()   # heatmap for selected cluster 

input_selectGO4 <- 'All'    #Gene set category 
# Read pathway data again 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO4,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  )  
results <- geneListBclustGO()  #Enrichment analysis for k-Means clusters    
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
adj.Pval Genes Pathways
6.8e-49 36 Photosynthesis
4.5e-37 23 Photosynthesis-light harvesting
2.6e-33 20 Photosystem I
1.3e-20 12 Photosystem I reaction center
7.2e-18 15 Photosystem II
3.1e-12 10 Photosystem II oxygen evolving complex
2.3e-09 8 Extrinsic component of membrane
1.5e-08 6 Fructose-bisphosphate aldolase activity
1.7e-07 32 Membrane
8.5e-06 8 Glycolytic process

10. Biclustering 3

input_nGenesBiclust <- 1000 #Top genes for biclustering
input_biclustMethod <- 'BCCC()' #Method: 'BCCC', 'QUBIC', 'runibic' ... 
biclustering.out = biclustering()  # run analysis

input_selectBicluster <- 3  #select a cluster 
biclustHeatmap()   # heatmap for selected cluster 

input_selectGO4 <- 'All'    #Gene set category 
# Read pathway data again 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO4,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  )  
results <- geneListBclustGO()  #Enrichment analysis for k-Means clusters    
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
adj.Pval Genes Pathways
2.3e-07 14 Transport
2.3e-07 7 Response to biotic stimulus
9.0e-07 11 Transporter activity
3.8e-06 7 Defense response
2.2e-03 4 Terpene synthase activity
5.7e-03 4 Lyase activity

11. Co-expression network

input_mySoftPower <- 25 #SoftPower to cutoff
input_nGenesNetwork <- 1000 #Number of top genes
input_minModuleSize <- 20   #Module size minimum 
wgcna.out = wgcna()   # run WGCNA  
## Warning: executing %dopar% sequentially: no parallel backend registered
##    Power SFT.R.sq    slope truncated.R.sq mean.k. median.k. max.k.
## 1      1 0.752000  1.50000          0.859   579.0     645.0    737
## 2      2 0.457000  0.52000          0.788   416.0     472.0    614
## 3      3 0.150000  0.20100          0.847   325.0     361.0    534
## 4      4 0.000364  0.00883          0.671   267.0     282.0    479
## 5      5 0.062400 -0.11200          0.658   226.0     225.0    437
## 6      6 0.175000 -0.19900          0.630   196.0     182.0    403
## 7      7 0.275000 -0.26700          0.661   172.0     152.0    376
## 8      8 0.359000 -0.31700          0.620   154.0     127.0    355
## 9      9 0.396000 -0.35700          0.653   139.0     107.0    336
## 10    10 0.444000 -0.38600          0.732   126.0      90.8    320
## 11    12 0.534000 -0.44600          0.788   107.0      65.8    293
## 12    14 0.586000 -0.47200          0.808    92.0      50.4    271
## 13    16 0.601000 -0.51500          0.864    80.7      39.1    252
## 14    18 0.638000 -0.53400          0.910    71.5      30.7    237
## 15    20 0.611000 -0.55700          0.862    64.1      23.9    223
## TOM calculation: adjacency..
## ..will not use multithreading.
##  Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
softPower()  # soft power curve 

modulePlot()  # plot modules  

listWGCNA.Modules.out = listWGCNA.Modules() #modules
input_selectGO5 <- 'All'    #Gene set category 
# Read pathway data again 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO5,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  ) 
input_selectWGCNA.Module <- '3. brown (198 genes)'  #Select a module
input_topGenesNetwork <- 10 #SoftPower to cutoff
input_edgeThreshold <- 0.4  #Number of top genes 
moduleNetwork() # show network of top genes in selected module
##  softConnectivity: FYI: connecitivty of genes with less than 16 valid samples will be returned as NA.
##  ..calculating connectivities..

input_removeRedudantSets <- TRUE    #Remove redundant gene sets 
results <- networkModuleGO()  #Enrichment analysis of selected module
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
adj.Pval Genes Pathways
3.9e-05 4 Cellular amino acid biosynthetic process
1.4e-04 12 Translation
1.4e-04 12 Ribosome
1.4e-04 12 Structural constituent of ribosome
3.2e-04 5 Unfolded protein binding
4.5e-04 3 S-adenosylmethionine biosynthetic process
4.5e-04 3 Methionine adenosyltransferase activity
1.3e-03 3 ATP metabolic process
2.2e-03 3 Proton-transporting two-sector ATPase complex-catalytic domain
2.5e-03 3 Hydrolase activity-acting on acid anhydrides-catalyzing transmembrane movement of substances

WGCNA GO2

input_selectGO5 <- 'All'    #Gene set category 
# Read pathway data again 
GeneSets.out <-readGeneSets( geneSetFile,
                             convertedData.out, input_selectGO5,input_selectOrg,
                             c(input_minSetSize, input_maxSetSize)  ) 
input_selectWGCNA.Module <- '2. blue (260 genes)'   #Select a module
input_topGenesNetwork <- 10 #SoftPower to cutoff
input_edgeThreshold <- 0.4  #Number of top genes 
moduleNetwork() # show network of top genes in selected module
##  softConnectivity: FYI: connecitivty of genes with less than 16 valid samples will be returned as NA.
##  ..calculating connectivities..

input_removeRedudantSets <- TRUE    #Remove redundant gene sets 
results <- networkModuleGO()  #Enrichment analysis of selected module
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE)
adj.Pval Genes Pathways
7.3e-08 9 Unfolded protein binding
1.2e-07 19 Intracellular
1.2e-06 18 Ribosome
1.2e-06 18 Structural constituent of ribosome
1.3e-06 18 Translation
3.6e-06 10 Protein folding
5.2e-03 5 Glycolytic process